{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-pose-sensitive-embedding-for-person-re","title":"A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking","arxiv_id":"1711.10378","date":"2017-11-28","proceeding":"CVPR 2018 6","authors":["M. Saquib Sarfraz","Arne Schumann","Andreas Eberle","Rainer Stiefelhagen"],"abstract":"Person re identification is a challenging retrieval task that requires\nmatching a person's acquired image across non overlapping camera views. In this\npaper we propose an effective approach that incorporates both the fine and\ncoarse pose information of the person to learn a discriminative embedding. In\ncontrast to the recent direction of explicitly modeling body parts or\ncorrecting for misalignment based on these, we show that a rather\nstraightforward inclusion of acquired camera view and/or the detected joint\nlocations into a convolutional neural network helps to learn a very effective\nrepresentation. To increase retrieval performance, re-ranking techniques based\non computed distances have recently gained much attention. We propose a new\nunsupervised and automatic re-ranking framework that achieves state-of-the-art\nre-ranking performance. We show that in contrast to the current\nstate-of-the-art re-ranking methods our approach does not require to compute\nnew rank lists for each image pair (e.g., based on reciprocal neighbors) and\nperforms well by using simple direct rank list based comparison or even by just\nusing the already computed euclidean distances between the images. We show that\nboth our learned representation and our re-ranking method achieve\nstate-of-the-art performance on a number of challenging surveillance image and\nvideo datasets.\n  The code is available online at:\nhttps://github.com/pse-ecn/pose-sensitive-embedding","url_abs":"http://arxiv.org/abs/1711.10378v2","url_pdf":"http://arxiv.org/pdf/1711.10378v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-pose-sensitive-embedding-for-person-re","repo_url":"https://github.com/pse-ecn/expanded-cross-neighborhood","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-pose-sensitive-embedding-for-person-re","repo_url":"https://github.com/pse-ecn/pose-sensitive-embedding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"re-ranking","task_name":"Re-Ranking"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-dukemtmc-reid","task":"Person Re-Identification","dataset":"DukeMTMC-reID","model":"PSE+ ECN (rank-dist)","rank_in_archive_order":46,"of":94,"metrics":{"Rank-1":"85.2","mAP":"79.8"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-mars","task":"Person Re-Identification","dataset":"MARS","model":"PSE+ ECN (rank-dist)","rank_in_archive_order":17,"of":21,"metrics":{"Rank-1":"76.7","mAP":"71.8"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-market-1501","task":"Person Re-Identification","dataset":"Market-1501","model":"PSE+ ECN (rank-dist)","rank_in_archive_order":93,"of":135,"metrics":{"Rank-1":"90.3","mAP":"84"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.10378","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.10378"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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